LOS ALAMITOS, Calif., June 28, 2013 /PRNewswire-iReach/ -- Big data has the power to change the world, from allowing faster medical diagnoses to coming up with cures for major diseases and from studying consumer purchasing decisions to better understanding human behavior and intention. Computer, IEEE Computer Society's flagship publication, this month delves deeper into these and other applications of this powerful technology with its "Big Data: New Opportunities and New Challenges" issue.

In the issue, guest editors Katina Michael of the University of Wollongong and Keith Miller of the University of Missouri-St. Louis describe several high-profile big data projects, such as the Human Brain Project and the US BRAIN Initiative to construct a supercomputer simulation of the brain to unlock the mysteries of Alzheimer's and Parkinson's. Those projects build on the success of the Human Genome Project, the 2003 effort that opened the door for extensive research into the genetic origins of disease and is a testament to the potential of big data. Michael and Miller note that big data can also be studied to help solve scientific problems in areas ranging from climatology to geophysics to nanotechnology.

Yet big data also represents some logistical—and ethical—challenges. For example, the increased use of video by companies and law enforcement for surveillance and investigation is creating a glut of data that is expensive to store and time-consuming to process. Corporations are using big data to learn more about their workforce, increase productivity, and introduce revolutionary business processes. However, these improvements come at a cost: continuously measuring employees' performance against industry benchmarks introduces a level of oversight that can quash the human spirit.

The guest editors note that although data mining in one form or another has occurred since people started to maintain records in the modern era, the volume and diversity of data requires ever-increasing processing speeds, yet must be stored economically and fed back into business-process life cycles in a timely manner.